75 lines
2.4 KiB
Python
75 lines
2.4 KiB
Python
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# Natural Language Toolkit: Text Trees
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#
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# Copyright (C) 2001-2023 NLTK Project
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# Author: Edward Loper <edloper@gmail.com>
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# Steven Bird <stevenbird1@gmail.com>
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# Peter Ljunglöf <peter.ljunglof@gu.se>
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# Tom Aarsen <>
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# URL: <https://www.nltk.org/>
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# For license information, see LICENSE.TXT
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from nltk.internals import raise_unorderable_types
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from nltk.probability import ProbabilisticMixIn
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from nltk.tree.immutable import ImmutableProbabilisticTree
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from nltk.tree.tree import Tree
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######################################################################
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## Probabilistic trees
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######################################################################
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class ProbabilisticTree(Tree, ProbabilisticMixIn):
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def __init__(self, node, children=None, **prob_kwargs):
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Tree.__init__(self, node, children)
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ProbabilisticMixIn.__init__(self, **prob_kwargs)
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# We have to patch up these methods to make them work right:
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def _frozen_class(self):
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return ImmutableProbabilisticTree
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def __repr__(self):
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return f"{Tree.__repr__(self)} (p={self.prob()!r})"
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def __str__(self):
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return f"{self.pformat(margin=60)} (p={self.prob():.6g})"
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def copy(self, deep=False):
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if not deep:
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return type(self)(self._label, self, prob=self.prob())
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else:
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return type(self).convert(self)
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@classmethod
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def convert(cls, val):
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if isinstance(val, Tree):
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children = [cls.convert(child) for child in val]
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if isinstance(val, ProbabilisticMixIn):
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return cls(val._label, children, prob=val.prob())
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else:
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return cls(val._label, children, prob=1.0)
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else:
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return val
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def __eq__(self, other):
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return self.__class__ is other.__class__ and (
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self._label,
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list(self),
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self.prob(),
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) == (other._label, list(other), other.prob())
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def __lt__(self, other):
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if not isinstance(other, Tree):
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raise_unorderable_types("<", self, other)
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if self.__class__ is other.__class__:
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return (self._label, list(self), self.prob()) < (
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other._label,
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list(other),
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other.prob(),
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)
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else:
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return self.__class__.__name__ < other.__class__.__name__
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__all__ = ["ProbabilisticTree"]
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